Several approaches have been developed to estimate age, an important aspect of forensics and orthodontics, using different measures and radiological examinations. Here, through meta-analysis, we determined the validity of age estimation methods and reproducibility of bone/dental maturity indices used for age estimation. The PubMed and Google Scholar databases were searched to December 31, 2021 for human cross-sectional studies meeting pre-defined PICOS criteria that simultaneously assessed the reproducibility and validity. Meta-estimates of validity (mean error: estimated age-chronological age) and intra- and inter-observer reproducibility (Cohen's kappa, intraclass correlation coefficient) and their predictive intervals (PI) were calculated using mixed-effect models when heterogeneity was high (I > 50%). The literature search identified 433 studies, and 23 met the inclusion criteria. The mean error meta-estimate (mixed effects model) was 0.08 years (95% CI - 0.12; 0.29) in males and 0.09 (95% CI - 0.12; 0.30) in females. The PI of each method spanned zero; of nine reported estimation methods, Cameriere's had the smallest (- 0.82; 0.47) and Haavikko's the largest (- 7.24; 4.57) PI. The reproducibility meta-estimate (fixed effects model) was 0.98 (95% CI 0.97; 1.00) for intra- and 0.99 (95% CI 0.98; 1.00) for inter-observer agreement. All methods were valid but with different levels of precision. The intra- and inter-observer reproducibility was high and homogeneous across studies.
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http://dx.doi.org/10.1038/s41598-022-19944-5 | DOI Listing |
Orphanet J Rare Dis
January 2025
Department of Pediatric Gastroenterology and Nutrition, Amsterdam UMC, Emma Children's Hospital, Vrije Universiteit, Amsterdam, The Netherlands.
Background: Achalasia is a rare esophageal motility disorder with an estimated annual incidence of 1-5/100.000 and a mean age at diagnosis > 50 years of age. Only a fraction of the patients has an onset during childhood (estimated incidence of 0.
View Article and Find Full Text PDFBMC Public Health
January 2025
Research Institute for Healthcare Policy, Korean Medical Association, Yongsan-gu, Seoul, South Korea.
Background: In 2024, the Korean Ministry of Health and Welfare enforced a policy to increase the number of medical school students by 2,000 over the next 5 years, despite opposition from doctors. This study aims to predict the trend of excess or shortage of medical personnel in Korea due to the policy of increasing the number of medical school students by 2035.
Methods: Data from multiple sources, including the Ministry of Health and Welfare, National Health Insurance Corporation, and the Korean Medical Association, were used to estimate supply and demand.
BMC Cardiovasc Disord
January 2025
Department of Cardiology, Affiliated Hospital of Hangzhou Normal University, Zhejiang Key Laboratory of Medical Epigenetics, School of Basic Medical Sciences,Hangzhou Institute of Cardiovascular Diseases, Engineering Research Center of Mobile Health Management System & Ministry of Education, Hangzhou Normal University, Hangzhou, 310015, China.
Objective: The effect of sodium intake on atrial fibrillation (AF)/atrial flutter (AFL), with respect to sex and age, has yet to be elucidated. This study aims to compare long-term trends in AF/AFL death and disability due to high sodium intake in China from 1990 to 2019.
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Lipids Health Dis
January 2025
Department of Cardiology, West China Hospital, Sichuan University West China School of Medicine, 37 Guoxue Road, Chengdu, Sichuan, 610041, China.
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View Article and Find Full Text PDFSci Rep
January 2025
Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland.
Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monitor real-time changes in cerebral hemodynamics. In this study, thirty-six healthy adults were measured by single channel fNIRS to explore differences between two age groups using machine learning (ML). The subjects, measured during functional magnetic resonance imaging (fMRI) at Oulu University Hospital, were divided into young (age ≤ 32) and elderly (age ≥ 57) groups.
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